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dc.contributor.authorMaiyar, Lohithaksha M.
dc.contributor.authorThakkar, Jitesh J.
dc.date.accessioned2020-06-23T09:59:41Z
dc.date.available2020-05-28T00:00:00Z
dc.date.available2020-06-23T09:59:41Z
dc.date.issued2019-05-28
dc.identifier.citationMaiyar LM, Thakkar JJ (2019) 'Environmentally conscious logistics planning for food grain industry considering wastages employing multi objective hybrid particle swarm optimization', Transportation Research Part E: Logistics and Transportation Review, 127 (), pp.220-248.en_US
dc.identifier.issn1366-5545
dc.identifier.doi10.1016/j.tre.2019.05.006
dc.identifier.urihttp://hdl.handle.net/10547/624075
dc.description.abstractThis paper develops a hub and spoke network based multi-objective green transportation model for evaluating optimal shipment quantity, modal choice, route selection, hub location, and vehicle velocity decisions considering wastages in Indian food grain context. A hybrid version of multi-objective meta-heuristic, Multi-Objective Particle Swarm Optimization with Differential Evolution (MOPSODE) is proposed to tackle the resulting non-linear formulation. Benchmarking with NSGA-II confirms the dominance of MOPSODE over NSGAII pertaining to near optimal pareto fronts obtained for the tested cases. Finally, the study derives the economic and environmental impact of varying hub location level, food grain wastage threshold and intermodal hub capacity.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S1366554518307701en_US
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectfood grain transportationen_US
dc.subjectMOPSODEen_US
dc.subjectsustainabilityen_US
dc.subjectmulti-objective optimizationen_US
dc.subjectGHG emissions reductionen_US
dc.subjectNSGA IIen_US
dc.titleEnvironmentally conscious logistics planning for food grain industry considering wastages employing multi objective hybrid particle swarm optimizationen_US
dc.typeArticleen_US
dc.contributor.departmentIndian Institute of Technology Kharagpuren_US
dc.contributor.departmentUniversity of Sheffielden_US
dc.identifier.journalTransportation Research Part E: Logistics and Transportation Reviewen_US
dc.date.updated2020-06-23T09:55:00Z
dc.description.note12m embargo


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